from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					42
 __A			       =					42







 def __init__(		self				:   Optional[Any]							, lowercase_				:   UNetaDModel							, lowercase_				:   ScoreSdeVeScheduler)						->	List[str]:


   """simple docstring"""







   super().__init__()
   self.register_modules(unet=lowercase_							, scheduler=lowercase_)







 @torch.no_grad()
 def __call__(		self				:   List[str]							, lowercase_				:   int = 1							, lowercase_				:   int = 2000							, lowercase_				:   Optional[Union[torch.Generator, List[torch.Generator]]] = None							, lowercase_				:   Optional[str] = "pil"							, lowercase_				:   bool = True							, **lowercase_				:   List[str]							, )						->	Union[ImagePipelineOutput, Tuple]:


   """simple docstring"""







   _UpperCamelCase           =    self.unet.config.sample_size
   _UpperCamelCase           =    (batch_size, 3, img_size, img_size)

   _UpperCamelCase           =    self.unet

   _UpperCamelCase           =    randn_tensor(lowercase_							, generator=lowercase_) * self.scheduler.init_noise_sigma
   _UpperCamelCase           =    sample.to(self.device)

   self.scheduler.set_timesteps(lowercase_)
   self.scheduler.set_sigmas(lowercase_)

   for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
     _UpperCamelCase           =    self.scheduler.sigmas[i] * torch.ones(shape[0]							, device=self.device)

     # correction step
     for _ in range(self.scheduler.config.correct_steps):
       _UpperCamelCase           =    self.unet(lowercase_							, lowercase_).sample
       _UpperCamelCase           =    self.scheduler.step_correct(lowercase_							, lowercase_							, generator=lowercase_).prev_sample

     # prediction step
     _UpperCamelCase           =    model(lowercase_							, lowercase_).sample
     _UpperCamelCase           =    self.scheduler.step_pred(lowercase_							, lowercase_							, lowercase_							, generator=lowercase_)

     _UpperCamelCase      ,						_UpperCamelCase           =    output.prev_sample, output.prev_sample_mean

   _UpperCamelCase           =    sample_mean.clamp(0							, 1)
   _UpperCamelCase           =    sample.cpu().permute(0							, 2							, 3							, 1).numpy()
   if output_type == "pil":
     _UpperCamelCase           =    self.numpy_to_pil(lowercase_)

   if not return_dict:
     return (sample,)

   return ImagePipelineOutput(images=lowercase_)





import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
   import torch

   from transformers.generation import (
       MaxLengthCriteria,
       MaxNewTokensCriteria,
       MaxTimeCriteria,
       StoppingCriteriaList,
       validate_stopping_criteria,
   )




@require_torch
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Dict)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    3
   _UpperCamelCase           =    250

   _UpperCamelCase           =    ids_tensor((batch_size, length)							, lowercase_)
   _UpperCamelCase           =    torch.ones((batch_size, length)							, device=lowercase_							, dtype=torch.float) / length
   return input_ids, scores







 def 		__UpperCAmelCase				(		self				:   Any)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)

   _UpperCamelCase           =    StoppingCriteriaList(
       [
           MaxLengthCriteria(max_length=10),
           MaxTimeCriteria(max_time=0.1),
       ])

   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    MaxLengthCriteria(max_length=10)

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	int:


   """simple docstring"""







   _UpperCamelCase           =    MaxNewTokensCriteria(start_length=5							, max_new_tokens=5)

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))

   _UpperCamelCase           =    StoppingCriteriaList([criteria])
   self.assertEqual(criteria_list.max_length							, 10)







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)

   _UpperCamelCase           =    MaxTimeCriteria(max_time=0.1)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase           =    MaxTimeCriteria(max_time=0.1							, initial_timestamp=time.time() - 0.2)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Tuple:


   """simple docstring"""







   validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])							, 10)

   with self.assertWarns(lowercase_):
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])							, 11)

   _UpperCamelCase           =    validate_stopping_criteria(StoppingCriteriaList()							, 11)

   self.assertEqual(len(lowercase_)							, 1)




